Heterogeneous Text Style Control Using Prompts

Advancements in natural language processing (NLP) have markedly improved paraphrase generation, an essential task for numerous applications. However, current methods face limitations due to model and constraint specificity, which hinder their flexibility and practical deployment. In this work, we introduce a unified prompt-driven approach to paraphrase generation that leverages diverse prompts, enabling fine-grained user control over aspects such as syntax and sentiment. Moreover, we incorporate translation to enable sophisticated cross-lingual text controls. Our system employs a data-centric paradigm which organizes prompts with natural language instructions. The proposed method is compatible with various sequence-to-sequence architectures and utilizes a novel training strategy to address the versatility of prompt combinations. Empirical results show that our approach not only demonstrates its capacity to adhere to multiple user-defined constraints but also maintains high performance in generation tasks without prompts. Moreover, extensive analysis shows that the model exhibits robustness to prompt variance such as language and quantity.